{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Fixed Variance process\n",
    "The `FixedVariance` volatility process can be used to implement zig-zag model \n",
    "estimation where two steps are repeated until convergence.  This can be used \n",
    "to estimate models which may not be easy to estimate as a single process due\n",
    "to numerical issues or a high-dimensional parameter space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This setup code is required to run in an IPython notebook_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "seaborn.set_style('darkgrid')\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"savefig\", dpi=90)\n",
    "plt.rc(\"font\",family=\"sans-serif\")\n",
    "plt.rc(\"font\",size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "Imports used in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "The VIX index will be used to illustrate the use of the `FixedVariance` process.  The data is from FRED and is provided by the `arch` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import arch.data.vix\n",
    "\n",
    "vix_data = arch.data.vix.load()\n",
    "vix = vix_data.vix.dropna()\n",
    "vix.name = 'VIX Index'\n",
    "ax = vix.plot(title='VIX Index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Mean Model Estimation\n",
    "The first step is to estimate the mean to filter the residuals using a constant variance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    HAR - Constant Variance Model Results                     \n",
      "==============================================================================\n",
      "Dep. Variable:              VIX Index   R-squared:                       0.876\n",
      "Mean Model:                       HAR   Adj. R-squared:                  0.876\n",
      "Vol Model:          Constant Variance   Log-Likelihood:               -2267.95\n",
      "Distribution:                  Normal   AIC:                           4545.90\n",
      "Method:            Maximum Likelihood   BIC:                           4571.50\n",
      "                                        No. Observations:                 1237\n",
      "Date:                Wed, Jan 29 2020   Df Residuals:                     1232\n",
      "Time:                        18:15:35   Df Model:                            5\n",
      "                                   Mean Model                                   \n",
      "================================================================================\n",
      "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
      "--------------------------------------------------------------------------------\n",
      "Const               0.6335      0.189      3.359  7.831e-04    [  0.264,  1.003]\n",
      "VIX Index[0:1]      0.9287  6.589e-02     14.095  4.056e-45    [  0.800,  1.058]\n",
      "VIX Index[0:5]     -0.0318  6.449e-02     -0.492      0.622  [ -0.158,9.463e-02]\n",
      "VIX Index[0:22]     0.0612  3.180e-02      1.926  5.409e-02 [-1.076e-03,  0.124]\n",
      "                            Volatility Model                            \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "sigma2         2.2910      0.396      5.782  7.361e-09 [  1.514,  3.068]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: White's Heteroskedasticity Consistent Estimator\n"
     ]
    }
   ],
   "source": [
    "from arch.univariate.mean import HARX, ZeroMean\n",
    "from arch.univariate.volatility import GARCH, FixedVariance\n",
    "\n",
    "mod = HARX(vix, lags=[1, 5, 22])\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Volatility Model Estimation\n",
    "Using the previously estimated residuals, a volatility model can be estimated using a `ZeroMean`. In this example, a GJR-GARCH process is used for the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Zero Mean - GJR-GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                  resid   R-squared:                       0.000\n",
      "Mean Model:                 Zero Mean   Adj. R-squared:                  0.001\n",
      "Vol Model:                  GJR-GARCH   Log-Likelihood:               -1936.93\n",
      "Distribution:                  Normal   AIC:                           3881.86\n",
      "Method:            Maximum Likelihood   BIC:                           3902.35\n",
      "                                        No. Observations:                 1237\n",
      "Date:                Wed, Jan 29 2020   Df Residuals:                     1233\n",
      "Time:                        18:15:36   Df Model:                            4\n",
      "                              Volatility Model                             \n",
      "===========================================================================\n",
      "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
      "---------------------------------------------------------------------------\n",
      "omega          0.2355  9.134e-02      2.578  9.932e-03  [5.647e-02,  0.415]\n",
      "alpha[1]       0.7217      0.374      1.931  5.353e-02 [-1.098e-02,  1.454]\n",
      "gamma[1]      -0.7217      0.252     -2.859  4.255e-03    [ -1.217, -0.227]\n",
      "beta[1]        0.5789      0.184      3.140  1.692e-03    [  0.218,  0.940]\n",
      "===========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "vol_mod = ZeroMean(res.resid.dropna(), volatility=GARCH(p=1, o=1, q=1))\n",
    "vol_res = vol_mod.fit(disp='off')\n",
    "print(vol_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = vol_res.plot('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-estimating the mean with a ``FixedVariance``\n",
    "The `FixedVariance` requires that the variance is provided when initializing the object.  The variance provided should have the same shape as the original data.  Since the variance estimated from the GJR-GARCH model is missing the first 22 observations due to the HAR lags, we simply fill these with 1.  These values will not be used to estimate the model, and so the value is not important. \n",
    "\n",
    "The summary shows that there is a single parameter, ``scale``, which is close to 1. The mean parameters\n",
    "have changed which reflects the GLS-like weighting that this re-estimation imposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 1936.9320776132129\n",
      "Iteration:      2,   Func. Count:     20,   Neg. LLF: 1936.2884244078432\n",
      "Iteration:      3,   Func. Count:     30,   Neg. LLF: 1936.173303940313\n",
      "Iteration:      4,   Func. Count:     39,   Neg. LLF: 1936.1217869245936\n",
      "Iteration:      5,   Func. Count:     48,   Neg. LLF: 1936.066653561499\n",
      "Iteration:      6,   Func. Count:     58,   Neg. LLF: 1936.063430293817\n",
      "Iteration:      7,   Func. Count:     65,   Neg. LLF: 1935.9527544039636\n",
      "Iteration:      8,   Func. Count:     72,   Neg. LLF: 1935.9470521933054\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 1935.947051582333\n",
      "            Iterations: 8\n",
      "            Function evaluations: 73\n",
      "            Gradient evaluations: 8\n",
      "                      HAR - Fixed Variance Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:              VIX Index   R-squared:                       0.876\n",
      "Mean Model:                       HAR   Adj. R-squared:                  0.876\n",
      "Vol Model:             Fixed Variance   Log-Likelihood:               -1935.95\n",
      "Distribution:                  Normal   AIC:                           3881.89\n",
      "Method:            Maximum Likelihood   BIC:                           3907.50\n",
      "                                        No. Observations:                 1237\n",
      "Date:                Wed, Jan 29 2020   Df Residuals:                     1232\n",
      "Time:                        18:15:37   Df Model:                            5\n",
      "                                    Mean Model                                    \n",
      "==================================================================================\n",
      "                      coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------------\n",
      "Const               0.5584      0.153      3.661  2.507e-04      [  0.260,  0.857]\n",
      "VIX Index[0:1]      0.9376  3.625e-02     25.866 1.607e-147      [  0.867,  1.009]\n",
      "VIX Index[0:5]     -0.0249  3.782e-02     -0.657      0.511 [-9.899e-02,4.926e-02]\n",
      "VIX Index[0:22]     0.0493  2.102e-02      2.344  1.909e-02  [8.064e-03,9.044e-02]\n",
      "                            Volatility Model                            \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "scale          0.9986  8.081e-02     12.358  4.420e-35 [  0.840,  1.157]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "variance = np.empty_like(vix)\n",
    "variance.fill(1.0)\n",
    "variance[22:] = vol_res.conditional_volatility**2.0\n",
    "fv = FixedVariance(variance)\n",
    "mod = HARX(vix, lags=[1, 5, 22], volatility=fv)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zig-Zag estimation\n",
    "A small repetitions of the previous two steps can be used to implement a so-called zig-zag estimation strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "                    HAR - Fixed Variance (Unit Scale) Model Results                    \n",
      "=======================================================================================\n",
      "Dep. Variable:                       VIX Index   R-squared:                       0.876\n",
      "Mean Model:                                HAR   Adj. R-squared:                  0.876\n",
      "Vol Model:         Fixed Variance (Unit Scale)   Log-Likelihood:               -1935.74\n",
      "Distribution:                           Normal   AIC:                           3879.48\n",
      "Method:                     Maximum Likelihood   BIC:                           3899.96\n",
      "                                                 No. Observations:                 1237\n",
      "Date:                         Wed, Jan 29 2020   Df Residuals:                     1233\n",
      "Time:                                 18:15:37   Df Model:                            4\n",
      "                                    Mean Model                                   \n",
      "=================================================================================\n",
      "                      coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "---------------------------------------------------------------------------------\n",
      "Const               0.5602      0.152      3.681  2.323e-04     [  0.262,  0.858]\n",
      "VIX Index[0:1]      0.9381  3.616e-02     25.940 2.388e-148     [  0.867,  1.009]\n",
      "VIX Index[0:5]     -0.0262  3.774e-02     -0.693      0.488   [ -0.100,4.781e-02]\n",
      "VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.810e-03,9.109e-02]\n",
      "=================================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(i)\n",
    "    vol_mod = ZeroMean(res.resid.dropna(), volatility=GARCH(p=1, o=1, q=1))\n",
    "    vol_res = vol_mod.fit(disp='off')\n",
    "    variance[22:] = vol_res.conditional_volatility**2.0\n",
    "    fv = FixedVariance(variance, unit_scale=True)\n",
    "    mod = HARX(vix, lags=[1, 5, 22], volatility=fv)\n",
    "    res = mod.fit(disp='off')\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct Estimation\n",
    "This model can be directly estimated.  The results are provided for comparison to the previous \n",
    "``FixedVariance`` estimates of the mean parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        HAR - GJR-GARCH Model Results                         \n",
      "==============================================================================\n",
      "Dep. Variable:              VIX Index   R-squared:                       0.876\n",
      "Mean Model:                       HAR   Adj. R-squared:                  0.875\n",
      "Vol Model:                  GJR-GARCH   Log-Likelihood:               -1932.61\n",
      "Distribution:                  Normal   AIC:                           3881.23\n",
      "Method:            Maximum Likelihood   BIC:                           3922.19\n",
      "                                        No. Observations:                 1237\n",
      "Date:                Wed, Jan 29 2020   Df Residuals:                     1229\n",
      "Time:                        18:15:38   Df Model:                            8\n",
      "                                   Mean Model                                   \n",
      "================================================================================\n",
      "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
      "--------------------------------------------------------------------------------\n",
      "Const               0.7796      1.190      0.655      0.513    [ -1.554,  3.113]\n",
      "VIX Index[0:1]      0.9180      0.291      3.156  1.597e-03    [  0.348,  1.488]\n",
      "VIX Index[0:5]     -0.0393      0.296     -0.133      0.894    [ -0.620,  0.541]\n",
      "VIX Index[0:22]     0.0632  6.353e-02      0.994      0.320 [-6.136e-02,  0.188]\n",
      "                            Volatility Model                            \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "omega          0.2357      0.250      0.944      0.345 [ -0.254,  0.725]\n",
      "alpha[1]       0.7091      1.069      0.664      0.507 [ -1.386,  2.804]\n",
      "gamma[1]      -0.7091      0.519     -1.367      0.172 [ -1.726,  0.308]\n",
      "beta[1]        0.5579      0.855      0.653      0.514 [ -1.117,  2.233]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "mod = HARX(vix, lags=[1, 5, 22], volatility=GARCH(1, 1, 1))\n",
    "res = mod.fit(disp='off')\n",
    "print(res.summary())"
   ]
  }
 ],
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